A stochastic multi‐period investment selection model to optimize strategic railway capacity planning
Bibliographic record
Abstract
SUMMARY North American Freight Railroads are approaching the limits of practical capacity because of substantial future demand. In this research, we develop a Stochastic Multi‐period Investment Selection Model (S‐MISM) to assist railroads best allocate their capital investments in the long‐term strategic capacity planning process. The novel optimization framework uses stochastic programming and Benders decomposition and provides a means to cope with unfulfilled demand and demand uncertainty in a long‐term multi‐period investment selection problem. S‐MISM can determine which portions of a rail network need to be upgraded with what kind of expansion options at each defined period in the planning horizon. Experimental results show that the inclusion of demand uncertainty results in a better and more robust capacity plan. Using this decision support tool will help railroads maximize their return from capacity expansion projects and minimize the risk in strategic capacity planning subject to demand uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".